Grid Clustering Research Articles

Patterns and drivers of zoogeographical regions of terrestrial vertebrates in China

AbstractAimZoogeographical regionalizations have recently seen a revived interest, which has provided new insights into biogeographical patterns. However, few quantitative studies have focused on zoogeographical regions of China. Here, we analyse zoogeographical regions for terrestrial vertebrates in China and how these regions relate to environmental and geological drivers and evaluate levels of cross‐taxon congruence.LocationChina.MethodsWe applied hierarchical clustering and non‐metric multidimensional scaling ordination to βsim dissimilarity matrices to delineate zoogeographical regions of China, based on the species distribution of 2102 terrestrial vertebrates in 50 × 50 km grid cells. We used generalized linear models and deviance partitioning to investigate the roles of current climate, past climate change, vegetation and geological processes in shaping the zoogeographical regions. Finally, we used Mantel and Kruskal–Wallis tests to evaluate the levels of cross‐taxon congruence.ResultsCluster analyses revealed 10 major zoogeographical regions: South China, the Yungui Plateau, Taiwan, North China, Northeast China, the Inner Mongolia Plateau, Northwest China, the Longzhong Plateau, the Tibetan Plateau and East Himalaya. In contrast to previous regionalizations, a major split was identified by clustering grid cell assemblages and dividing the eastern and western parts of China, followed by the northern part of China. Deviance partitioning showed that current climate and geological processes explained most of the deviance both jointly and independently. Congruence in species composition of endotherms and ectotherms was surprisingly low.Main conclusionsWe propose new zoogeographical regions for China based on our quantitative methods. In contrast to previous regionalizations, we consider Central China as a part of South China and identify the Longzhong Plateau and Taiwan as independent regions. While our results strongly support the notion of a broad biogeographical transition zone in East Asia, they also suggest a major south–north‐oriented Palaearctic‐Oriental boundary in China.

Implementation of the Open Source Virtualization Technologies in Cloud Computing

The “Virtualization and Cloud Computing” is a recent buzzword in the digital world. Behind this fancy poetic phrase there lies a true picture of future computing for both in technical and social perspective. Though the “Virtualization and Cloud Computing are recent but the idea of centralizing computation and storage in distributed data centres maintained by any third party companies is not new but it came in way back in 1990s along with distributed computing approaches like grid computing, Clustering and Network load Balancing. Cloud computing provide IT as a service to the users on-demand basis. This service has greater flexibility, availability, reliability and scalability with utility computing model. This new concept of computing has an immense potential in it to be used in the field of e-governance and in the overall IT development perspective in developing countries like Bangladesh.

The Effect of UsefulVisitingof Social Commerce Consumers Based on Grid Clustering

Grid‐based Clustering and semaphores have garnered improbable interest from both securityexperts and information theorists in the lastseveral years. In fact, few leading analystswould disagree with the effect of useful visiting of social commerce consumers, which embodies the typical principlesof e‐commerce. In this paper, weverify that not only model checking and evolutionary programming can cooperate to solve this problem, but also improve the networks practically.

An Execution Framework for Grid-clustering Methods

Cluster analysis methods have proven extremely valuable for explorative data analysis and are also fundamental for data mining methods. Goal of cluster analysis is to find correlations of the value space and to separate the data values into a priori unknown set of subgroups based on a similarity metrics. In case of high dimensional data, i.e. data with a large number of describing attributes, clustering can result into a very time consuming task, which often limits the number of observations to be clustered in practice. To overcome this problem, Grid clustering methods have been developed, which do not calculate similarity values between the data value each, but organize the value space surrounding the data values, e.g. by specific data structure indices. In this paper we present a framework which allows to evaluate different data structures for the generation of a multi-dimensional grid structure grouping the data values into blocks. The values are then clustered by a topological neighbor search algorithm on the basis of the block structure. As first data structure to be evaluated we present the BANG file structure and show its feasibility as clustering index. The developed framework is planned to be contributed as package to the WEKA software.

Modified Grid Clustering Technique to Predict Heat Transfer Coefficient in a Duct of Arbitrary Cross Section Area

Modified Grid Clustering Technique to Predict Heat Transfer Coefficient in a Duct of Arbitrary Cross Section Area

Topology-based clustering using polar self-organizing map.

Cluster analysis of unlabeled data sets has been recognized as a key research topic in varieties of fields. In many practical cases, no a priori knowledge is specified, for example, the number of clusters is unknown. In this paper, grid clustering based on the polar self-organizing map (PolSOM) is developed to automatically identify the optimal number of partitions. The data topology consisting of both the distance and density is exploited in the grid clustering. The proposed clustering method also provides a visual representation as PolSOM allows the characteristics of clusters to be presented as a 2-D polar map in terms of the data feature and value. Experimental studies on synthetic and real data sets demonstrate that the proposed algorithm provides higher clustering accuracy and lower computational cost compared with six conventional methods.

A REVIEW TO ASSESS OPPORTUNITIES AND SECURITY RISK CHALLENGES IN CLOUD COMPUTING

In today’s competitive world, organizations are under increasing pressure to develop efficiently and convert their IT practices to achieve more with less. Cloud Computing is a flexible technology that can support a wide variety of application. In contrast to grid computing and clustering, cloud computing is service oriented, loosely coupled, TCP/IP based Business Model technology that provides high security and advantage of virtualization and strong fault tolerance. The purpose of the paper is to provide a review of the cloud computing concepts and challenges facing organizations in its adoption

Mixed convection in horizontal duct flow with transverse magnetic field and heating of side wall

Computational study of mixed convection in a horizontal duct with very strong (Hartmann numbers Ha up to 800) horizontal transverse magnetic field and electrically insulated walls is conducted. Strong constant-rate heating (Grashof numbers Gr up to 109) is applied to one vertical wall, while the other walls are thermally insulated. It is found that in a wide range of Ha and Gr the flow is two-dimensional (uniform in the streamwise direction) and steady-state. No instability to convection rolls aligned with the magnetic field detected recently in a similar duct and pipe with bottom heating is observed. The velocity field is a combination of convection-modified streamwise flow and convection-induced circulation in the transverse plane. The circulation has strong jets near the heated and horizontal walls as prominent features. It is demonstrated that, at a constant Reynolds number, integral characteristics and spatial structure of the flow can be approximately considered as determined by Gr/Ha2 as a single parameter. We also present results of a systematic comparison study of several commonly used grid clustering schemes in their application to wall-bounded flows with strong magnetic fields.

IOG - An Improved Approach to Find Optimal Grid Size Using Grid Clustering Algorithm

The grid-clustering algorithm is the most important type in the hierarchical clustering algorithm. The grid-based clustering approach considers cells rather than data points. In grid-based clustering, all the clustering operations are performed on the segmented data space, rather than the original data objects. Grid- based methods are highly popular compared to the other conventional models due to their computational efficiency but to find optimal grid size is a key feature in grid-based clustering algorithm. There exist some algorithm in that they achieve optimal grid size but in real life data can be dense or sparse. So, in these research to develop an algorithm that can find optimal grid size in any type of dataset in dense or sparse with appropriate accuracy or maintaining the accuracy with less time.

Numerical Methods for Two-Dimensional Stem Cell Tissue Growth.

Growth of developing and regenerative biological tissues of different cell types is usually driven by stem cells and their local environment. Here, we present a computational framework for continuum tissue growth models consisting of stem cells, cell lineages, and diffusive molecules that regulate proliferation and differentiation through feedback. To deal with the moving boundaries of the models in both open geometries and closed geometries (through polar coordinates) in two dimensions, we transform the dynamic domains and governing equations to fixed domains, followed by solving for the transformation functions to track the interface explicitly. Clustering grid points in local regions for better efficiency and accuracy can be achieved by appropriate choices of the transformation. The equations resulting from the incompressibility of the tissue is approximated by high-order finite difference schemes and is solved using the multigrid algorithms. The numerical tests demonstrate an overall spatiotemporal second-order accuracy of the methods and their capability in capturing large deformations of the tissue boundaries. The methods are applied to two biological systems: stratified epithelia for studying the effects of two different types of stem cell niches and the scaling of a morphogen gradient with the size of the Drosophila imaginal wing disc during growth. Direct simulations of both systems suggest that that the computational framework is robust and accurate, and it can incorporate various biological processes critical to stem cell dynamics and tissue growth.

Framework of Persistence Layer Synchronous Replication to Improve Data Availability into a Heterogeneous System

Data replication is an important technique in peer to peer network, data grid architecture, clustering and distributed system, where it increases data availability and enhances data access and reliability and minimizes the cost of data transmission. In this paper, we proposed a framework and structure of synchronous replication from the persistence layer that supports heterogeneous system. In this framework, we developed the multithreading based persistence layer. Our objective is to make the persistence layer more adaptive. In this adaptive persistency system, the replication server will not depend on the main server, so forth, adding a new replication server will be easier than ever, easy to cope with heterogeneous system, cost minimizing and finally there will be no down time.

On the estimation of arterial route travel time distribution with Markov chains

Recent advances in the probe vehicle deployment offer an innovative prospect for research in arterial travel time estimation. Specifically, we focus on the estimation of probability distribution of arterial route travel time, which contains more information regarding arterial performance measurements and travel time reliability. One of the fundamental contributions of this work is the integration of travel time correlation of route’s successive links within the methodology. In the proposed technique, given probe vehicles travel times of the traversing links, a two-dimensional (2D) diagram is established with data points representing travel times of a probe vehicle crossing two consecutive links. A heuristic grid clustering method is developed to cluster each 2D diagram to rectangular sub spaces (states) with regard to travel time homogeneity. By applying a Markov chain procedure, we integrate the correlation between states of 2D diagrams for successive links. We then compute the transition probabilities and link partial travel time distributions to obtain the arterial route travel time distribution. The procedure with various probe vehicle sample sizes is tested on two study sites with time dependent conditions, with field measurements and simulated data. The results are very close to the Markov chain procedure and more accurate once compared to the convolution of links travel time distributions for different levels of congestion, even for small penetration rates of probe vehicles.

Orientation domains: A mobile grid clustering algorithm with spherical corrections

An algorithm has been designed and tested which was devised as a tool assisting the analysis of geological structures solely from orientation data. More specifically, the algorithm was intended for the analysis of geological structures that can be approached as planar and piecewise features, like many folded strata. Input orientation data is expressed as pairs of angles (azimuth and dip). The algorithm starts by considering the data in Cartesian coordinates. This is followed by a search for an initial clustering solution, which is achieved by comparing the results output from the systematic shift of a regular rigid grid over the data. This initial solution is optimal (achieves minimum square error) once the grid size and the shift increment are fixed. Finally, the algorithm corrects for the variable spread that is generally expected from the data type using a reshaped non-rigid grid. The algorithm is size-oriented, which implies the application of conditions over cluster size through all the process in contrast to density-oriented algorithms, also widely used when dealing with spatial data. Results are derived in few seconds and, when tested over synthetic examples, they were found to be consistent and reliable. This makes the algorithm a valuable alternative to the time-consuming traditional approaches available to geologists.

Combining an Angle Criterion with Voxelization and the Flying Voxel Method in Reconstructing Building Models from LiDAR Data

Abstract: Traditional documentation capabilities of laser scanning technology can be further exploited for urban modeling through the transformation of resulting point clouds into solid models compatible for computational analysis. This article introduces such a technique through the combination of an angle criterion and voxelization. As part of that, a k‐nearest neighbor (kNN) searching algorithm is implemented using a predefined number of kNN points combined with a maximum radius of the neighborhood, something not previously implemented. From this sample, points are categorized as boundary or interior points based on an angle criterion. Façade features are determined based on underlying vertical and horizontal grid voxels of the feature boundaries by a grid clustering technique. The complete building model involving all full voxels is generated by employing the Flying Voxel method to relabel voxels that are inside openings or outside the façade as empty voxels. Experimental results on three different buildings, using four distinct sampling densities showed successful detection of all openings, reconstruction of all building façades, and automatic filling of all improper holes. The maximum nodal displacement divergence was 1.6% compared to manually generated meshes from measured drawings. This fully automated approach rivals processing times of other techniques with the distinct advantage of extracting more boundary points, especially in less dense data sets (<175 points/m2), which may enable its more rapid exploitation of aerial laser scanning data and ultimately preclude needing a priori knowledge.

Heat transfer in internal channel of a blade: Effects of rotation in a trailing edge cooling system

The aerothermal performance of a trailing edge (TE) internal cooling system of a high pressure gas turbine blade was evaluated under stationary and rotating conditions. The investigated geometry consists of a 30:1 scaled model reproducing a typical wedge shaped discharge duct with one row of enlarged pedestals. The airflow pattern inside the device simulates a highly loaded rotor blade cooling scheme with a 90 [deg] turning flow from the radial hub inlet to the tangential TE outlet. Two different tip configurations were tested, the first one with a completely closed section, the second one with a 5 holes outlet surfaces discharging at ambient pressure. In order to assess rotation effects, a rotating test rig, composed of a rotating arm holding both the PMMA TE model and the instrumentation, was purposely developed and manufactured. A thin Inconel heating foil and wide band Thermo-chromic Liquid Crystals are used to perform steady state heat transfer measurements on the blade pressure side. A rotary joint ensures the pneumatic connection between the blower and the rotating apparatus; moreover several slip rings are used for both instrumentation power supply and thermocouple connection. A parallel CFD analysis involving steady-state RANS modeling was conducted to allow an insight of the flow field inside the redirecting channel and the interpedestal ducts to better interpret the developing vortical structures. Low-Reynolds grid clustering permits to integrate up to the wall both the momentum and the thermal boundary layer. Calculations were performed by means of an in-house developed pressure based solver exploiting the k-ω SST turbulence model implemented in the framework of the open-source finite volume discretization toolbox OpenFOAM®. Analyzed flow conditions correspond to Reynolds number of 20000 in the hub inlet section and angular speed varies to obtain rotation numbers in the range from 0 to 0.3. The orientation of the rotation axis is orthogonal to the heated surface as to resemble a 90 [deg] blade metal angle. Results are reported in terms of detailed heat transfer coefficient 2D maps on the suction side surface as well as spanwise profiles inside the pedestal ducts.

A New Elliptical Grid Clustering Method

Abstract A new base on grid clustering method is presented in this paper. This new method first does unsupervised learning on the high dimensions data. This paper proposed a grid-based approach to clustering. It maps the data onto a multi-dimensional space and applies a linear transformation to the feature space instead of to the objects themselves and then approach a grid-clustering method. Unlike the conventional methods, it uses a multidimensional hyper-eclipse grid cell. Some case studies and ideas how to use the algorithms are described. The experimental results show that EGC can discover abnormity shapes of clusters.

Adjustable Probability Density Grid-Based Clustering for Uncertain Data Streams

Most existing traditional grid-based clustering algorithms for uncertain data streams that used the fixed meshing method have the disadvantage of low clustering accuracy. In view of above deficiencies, this paper proposes a novel algorithm APDG-CUStream, Adjustable Probability Density Grid-based Clustering for Uncertain Data Streams, which adopts the online component and offline component. In online component, the Probability Density Grid Clustering Feature is defined to store the summary information of uncertain data streams, and the time decay factor that introduced into the definition of the probability can reduce the influence of outdated data on clustering results. Init_clustering algorithm is called at special time interval in offline component, it first adjusts sparse probability density grid unit and updates the clustering feature of all probability density grid units. For dense probability density grid, we find and merge all dense or medium neighboring probability density grids connected with this dense probability density grid, and then the Init_clustering results is obtained. Finally APDG-CUStream returns final clustering results. The experimental results show that APDGCUStream algorithm can accurately and rapidly obtain the clustering results with arbitrary shapes and also get better clustering quality.

A gossip-like distributed optimization algorithm for reactive power flow control

AbstractWe considered the problem of minimizing reactive power flows in a smart microgrid. First we modeled this problem as a linearly constrained quadratic optimization, in which the decision variables are the amount of reactive power that compensators inject into the network. Then, we designed a distributed algorithm in which agents are clustered into overlapping subsets according to a given communication graph; at each time, agents belonging to a randomly chosen subset update their states in order to minimize the reactive power flows on the grid. We showed that, by sensing the network at their points of connection, agents can perform this minimization with just the data that they can gather from the other agents belonging to the subset. We characterized the convergence of this algorithm and we studied its rate of convergence.We finally analyzed some specific grid topologies and clustering choices.

The role of climate and plant functional trade-offs in shaping global biome and biodiversity patterns

Aim: Two of the oldest observations in plant geography are the increase in plant diversity from the poles towards the tropics and the global geographic distribution of vegetation physiognomy (biomes). The objective of this paper is to use a process-based vegetation model to evaluate the relationship between modelled and observed global patterns of plant diversity and the geographic distribution of biomes. Location: The global terrestrial biosphere. Methods: We implemented and tested a novel vegetation model aimed at identifying strategies that enable plants to grow and reproduce within particular climatic conditions across the globe. Our model simulates plant survival according to the fundamental ecophysiological processes of water uptake, photosynthesis, reproduction and phenology. We evaluated the survival of an ensemble of 10,000 plant growth strategies across the range of global climatic conditions. For the simulated regional plant assemblages we quantified functional richness, functional diversity and functional identity. Results: A strong relationship was found (correlation coefficient of 0.75) between the modelled and the observed plant diversity. Our approach demonstrates that plant functional dissimilarity increases and then saturates with increasing plant diversity. Six of the major Earth biomes were reproduced by clustering grid cells according to their functional identity (mean functional traits of a regional plant assemblage). These biome clusters were in fair agreement with two other global vegetation schemes: a satellite image classification and a biogeography model (kappa statistics around 0.4). Main conclusions: Our model reproduces the observed global patterns of plant diversity and vegetation physiognomy from the number and identity of simulated plant growth strategies. These plant growth strategies emerge from the first principles of climatic constraints and plant functional trade-offs. Our study makes important contributions to furthering the understanding of how climate affects patterns of plant diversity and vegetation physiognomy from a process-based rather than a phenomenological perspective.

Performance of an Energy Efficient Routing Scheme for Cluster-based Wireless Sensor Networks

This paper proposes a novel energy efficient data clustering scheme to improve energy efficiency for cluster-based wireless sensor networks (WSNs). In order to reduce the energy dissipation of transmitting sensing data at each sensor, the fixed clustering algorithm uniformly divides the sensing area into clusters where the cluster head is deployed to the centered of the cluster area. Moreover, to perform energy efficient grid clustering (EEGC), the nodes take turns to be cluster head. Simulation results show that proposed EEGC definitely reduces the energy consumption of the sensors and prolongs network lifetime to be longer than LEACH for both life rate=50% and 70% . Moreover, the total transmission data of EEGC is more 60% than LEACH scheme.